Deep sea spy: a collaborative annotation tool
Bibliographic record
Abstract
Since 2010, remote hydrothermal ecosystems are continuously being monitored using video cameras deployed on instrumented platforms. The acquisition of high-frequency video data from deep-sea observatories like EMSOAzores or Ocean Networks Canada provide information on species behaviour, feeding habits, growth, reproduction and organisms’ response to changes in environmental conditions. Video cameras acquire hourly data representing thousands of hours and Tera Bytes of footage but their manual processing is time-consuming and highly labour-intensive, and cannot be comprehensively undertaken by individual researchers. In order to help preliminary manual assessment of this huge imagery archive, a free online annotation tool was developed to gather contributions from a wider community. The Deep Sea Spy system offers a fun and engaging web interface to members of the public to help perform initial footage annotations. The platform now hosts 623 active annotators who contributed 179,663 annotations to 19,541 images. Preliminary analyses highlight a high variability among participants but show promising results to detect trends in species abundance variation over time. Ultimately, the information gathered via this approach can help improving the algorithms necessary to produce accurate automated detection in imagery using a machine learning approach.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.014 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".